Method for expanding target user group, electronic device and storage medium

Abstract

The present invention relates to the field of computer technology, and in particular to a method for expanding a target user group, an electronic device, and a storage medium. The method comprises: extracting a plurality of product keywords corresponding to a first number of product description texts corresponding to a given user according to a product recommendation order of a target app of a given user, matching the plurality of product keywords corresponding to the given user with a plurality of preset keywords in a preset keyword library to determine a target product subtag corresponding to the given user; when the number of users corresponding to the target product subtag is less than a threshold number of users corresponding to the target product subtag, training a preset logistic regression model, inputting the plurality of product keywords corresponding to each given user into the trained target logistic regression model, and selecting a plurality of given users not less than the threshold number of users according to the output result and determining them as target users; the present invention can achieve precise expansion of the target user group.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method for expanding a target user group, an electronic device, and a storage medium. Background Art

[0002] When recommending products in the target app, in order to improve the accuracy of product recommendations, it is necessary to set corresponding product preference labels for each device, that is, each user. The product labels of existing apps are generally set based on the user's usage behavior data of the app, but the current product label division is not detailed enough. For example, the mother and baby label is divided into multiple age groups as sub-labels. Each sub-label covers a large age range, which affects the accurate recommendation of products. After the product labels are subdivided, the user level of some sub-labels will not meet the existing market demand. Therefore, there is an urgent need to provide a reasonable method for expanding the user base. Summary of the Invention

[0003] In response to the above technical problems, the present invention provides a method for expanding the target user group, an electronic device and a storage medium, which can improve the accuracy of matching preset product sub-tags with users, while also increasing the number of users corresponding to each preset product sub-tag, thereby achieving accurate and reasonable expansion of the user group.

[0004] According to a first aspect of the present invention, a method for expanding a target user group is provided, comprising the following steps:

[0005] According to the product recommendation order of the target app of each given user, the first number of product description texts corresponding to each given user are obtained, and a number of product keywords corresponding to each product description text are extracted.

[0006] For any given user, several product keywords corresponding to the given user are matched with several preset keywords in the preset keyword library, and based on the preset product sub-tags corresponding to the matched several preset keywords, the target product sub-tag corresponding to the given user is determined; the preset keyword library is a library composed of several preset keywords corresponding to several preset product sub-tags.

[0007] When the number of users corresponding to the target product sub-tag is less than the threshold number of users corresponding to the target product sub-tag, according to the given users corresponding to each target product sub-tag, positive samples and negative samples are divided from the product description texts corresponding to the given users to train the preset logistic regression model and obtain the target logistic regression model.

[0008] Input several product keywords corresponding to each given user into the target logistic regression model, output the number of given users corresponding to each preset product sub-label, and at the same time output the confidence level corresponding to each given user.

[0009] According to the user quantity threshold corresponding to each preset product sub-tag, a number of given users whose number is not less than the user quantity threshold are selected in descending order of confidence and are all determined as target users to complete the expansion of the target user group.

[0010] According to a second aspect of the present invention, a non-transitory computer-readable storage medium is provided, in which at least one instruction or at least one program is stored. The at least one instruction or the at least one program is loaded and executed by a processor to implement the above-mentioned method for expanding the target user group.

[0011] According to a third aspect of the present invention, there is provided an electronic device comprising a processor and the above-mentioned non-transitory computer-readable storage medium.

[0012] The present invention has at least the following beneficial effects:

[0013] The present invention provides a method for expanding a target user group. First, according to the product recommendation order of a target app of a given user, a number of product keywords corresponding to the first number of product description texts corresponding to the given user are extracted, the number of product keywords corresponding to the given user are matched with a number of preset keywords in a preset keyword library, and a target product sub-tag corresponding to the given user is determined. By determining the target product sub-tag, the accuracy of matching the preset product sub-tag with the user can be improved. When the number of users corresponding to the target product sub-tag is less than a threshold value of the number of users corresponding to the target product sub-tag, a preset logistic regression model is trained. By dividing positive samples and negative samples from the product description text corresponding to the given user, keyword expansion can be achieved. The expanded keywords are learned through the preset logistic regression model, so that the obtained target logistic regression model can screen out more users for each preset product sub-tag, thereby achieving reasonable and accurate expansion of the user group. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a flow chart of a method for expanding a target user group provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] This embodiment provides a method for expanding the number of target user groups, such as Figure 1 As shown, the method includes the following steps:

[0018] S100, according to the product recommendation order of the target app of each given user, obtain the first first number of product description texts corresponding to each given user, and extract several product keywords corresponding to each product description text; technical personnel in this field set the first number according to actual needs, for example, 20.

[0019] Specifically, the product description text refers to the title text corresponding to any product in the first first number of products corresponding to a given user, for example, the product title on the page after clicking on any product in a shopping app.

[0020] Furthermore, the product keyword is any keyword obtained after word segmentation processing of the product description text according to a word segmentation tool.

[0021] As described above, since the products that the user is interested in will be recommended to the front based on the user's usage behavior of the target app, when evaluating the user's product sub-tags, obtaining the description texts of the first number of recommended products can make the subsequent determination of the target product sub-tags for a given user more accurate and reliable.

[0022] S200, for any given user, several product keywords corresponding to the given user are matched with several preset keywords in the preset keyword library, and based on the preset product sub-tags corresponding to the matched several preset keywords, the target product sub-tag corresponding to the given user is determined.

[0023] Specifically, the preset keyword library is composed of several preset keywords corresponding to several preset product sub-tags; it can be understood that each preset product sub-tag corresponds to several preset keywords. In an implementable scenario, the maternity and baby label is divided into several product sub-tags, such as pregnancy, newborn, 1-6 months, 6-12 months, 1-2 years old, 2-3 years old, etc. The preset keywords corresponding to pregnancy include pregnant women, folic acid, delivery bag, maternity pad, fetal heart monitor, etc. The keywords corresponding to 1-2 years old include stage 3 milk powder, toddler shoes, baby picture books, etc., which will not be repeated here.

[0024] Furthermore, several preset keywords corresponding to each preset product sub-tag are obtained through the following steps:

[0025] S10, obtaining the first several target products under the target label category; the target label is the upper level label of several preset product sub-labels; it can be understood that: under the target label, several preset product sub-labels are further divided, for example, the target label is the above-mentioned mother and baby label.

[0026] S20, performing word segmentation processing on the title text of each target product to obtain a number of initial keywords corresponding to each target product.

[0027] S30 , screening out a number of preset keywords from all the initial keywords according to the TF-IDF value corresponding to each initial keyword and a preset TF-IDF threshold.

[0028] S40, receiving the preset product subtag corresponding to each preset keyword to obtain several preset keywords corresponding to each preset product subtag; it can be understood as: based on the manual analysis results, receiving the preset product subtag belonging to each preset keyword sent by the background user.

[0029] In a specific embodiment, determining the target product subtag corresponding to a given user based on the preset product subtags corresponding to the matched plurality of preset keywords includes the following steps:

[0030] S201, based on the preset priority corresponding to each preset product sub-tag, obtain the tag priority corresponding to each preset product sub-tag among the preset product sub-tags corresponding to the plurality of preset keywords that match. In a specific implementation, for example, for the plurality of preset product sub-tags under the maternity and baby tag, the younger the child, the higher the priority of the product sub-tag.

[0031] S202: Determine the preset product sub-tag corresponding to the highest tag priority as the target product sub-tag corresponding to the given user.

[0032] As mentioned above, after each given user matches several preset product sub-tags, considering that at the current stage, the user will search for and purchase products for the next growth stage as a backup, but will not purchase products for the previous stage, the preset product sub-tags are prioritized according to age groups, and the preset product sub-tag with the highest priority is used as the target product sub-tag. This can improve the accuracy of matching product sub-tags with users, which is conducive to subsequent accurate product recommendations to users.

[0033] In another specific embodiment, the step of determining the target product subtag corresponding to a given user based on the preset product subtags corresponding to the matched plurality of preset keywords further includes the following steps:

[0034] S210 , according to the preset tag priority corresponding to each preset product sub-tag, priority sorting is performed on the preset product sub-tags corresponding to the matched plurality of preset keywords to obtain a sorting result.

[0035] S220, according to a preset order-score comparison table, obtain the order score corresponding to each preset product sub-tag in the sorting result; it can be understood that: the higher the priority corresponding to the preset product sub-tag, the higher its corresponding order score.

[0036] S230: Obtain the number of recommended products corresponding to each preset product subtag in the sorted result from the first number of recommended products corresponding to the given user. For example, if 5 of the first 20 recommended products for the given user match a preset product subtag, the number of recommended products corresponding to the preset product subtag is 5.

[0037] S240, calculating a weighted sum based on the order score, the number of recommended products, and the preset weights corresponding to the order score and the number of recommended products to obtain a final score corresponding to each preset product sub-tag in the sorting result.

[0038] Furthermore, the preset weight corresponding to the order score is greater than the preset weight corresponding to the number of product recommendations; since the priority corresponding to the preset product sub-tag plays a dominant role in the classification results, a higher weight is set for the order score.

[0039] S250: Determine the preset product sub-tag corresponding to the maximum final score as the target product sub-tag corresponding to the given user.

[0040] As mentioned above, since the priority corresponding to the preset product sub-tag has a greater impact on the classification results, but since the number of product recommendations is also recommended based on user behavior, it also reflects the user's current product needs to a certain extent. Therefore, the target product sub-tag determined based on the combination of the two is more suitable for a given user, further improving the accuracy of labeling for users.

[0041] S300, when the number of users corresponding to the target product sub-tag is less than the threshold value of the number of users corresponding to the target product sub-tag, according to the given users corresponding to each target product sub-tag, positive samples and negative samples are divided from the several product description texts corresponding to the several given users to train the preset logistic regression model to obtain the target logistic regression model; it can be understood that: the output layer of the preset logistic regression model is constructed according to several preset product sub-tags to achieve the purpose of multi-classification.

[0042] Specifically, in step S300, the target logistic regression model is obtained through the following steps:

[0043] S301: For any target product subtag, a second number of given users are randomly selected from the given users corresponding to the target product subtag, and the product description texts of the given users are divided into training and test sets according to a preset ratio. Those skilled in the art may set the second number based on actual needs, for example, selecting 320,000 pieces of data for each preset product subtag.

[0044] Furthermore, the ratio of training set to test set is 9:1.

[0045] S302, the product keywords in the product description text corresponding to the target product sub-label in the training set are determined as positive samples, and the product keywords in the remaining product description texts are determined as negative samples; in the specific implementation, before determining the product keywords as positive samples and negative samples, the product keywords need to be deduplicated.

[0046] S303: Input the feature vectors corresponding to the positive samples and the feature vectors corresponding to the negative samples into a preset logistic regression model for training to obtain an initial logistic regression model. For example, when the target product sub-label is pregnancy, the positive samples include crib, baby, etc., and the negative samples include teether tips, etc.

[0047] At step S304, several product keywords corresponding to each product description in the test set are input into an initial logistic regression model. The initial logistic regression model parameters are adjusted based on the output results to obtain a target logistic regression model. The model parameter adjustment process utilizes existing techniques or is performed based on the actual needs of those skilled in the art.

[0048] As mentioned above, the target product sub-tag assigned to a given user is already relatively accurate, but in the actual implementation process, there is still a situation where the magnitude of the number of users corresponding to the preset product sub-tag does not meet business needs. Therefore, the present invention can achieve keyword expansion by dividing positive samples and negative samples from the product description text corresponding to the given user, and learn the expanded keywords through a preset logistic regression model, so that the obtained target logistic regression model can screen out more users for each preset product sub-tag, thereby achieving reasonable and accurate expansion of the user group.

[0049] At step S400, the target logistic regression model is used to input the product keywords corresponding to each given user into the target logistic regression model. The target logistic regression model outputs the number of given users corresponding to each preset product sub-tag and the confidence level corresponding to each given user. In a specific embodiment, since the target logistic regression model is a multi-classification model constructed based on the preset product sub-tags, the target logistic regression model outputs the preset product sub-tag assigned to each given user and the classification confidence level for each given user.

[0050] Furthermore, after outputting the given number of users corresponding to each preset product sub-label, the method further includes the following steps:

[0051] S410, when the number of given users corresponding to the preset product sub-tag is less than the threshold number of users corresponding to the preset product sub-tag, return to the execution step of dividing positive samples and negative samples from several product description texts corresponding to all given users according to the given users corresponding to each target product sub-tag, so as to train the preset logistic regression model and obtain the target logistic regression model until the output number of given users corresponding to each preset product sub-tag is not less than the threshold number of users corresponding to the preset product sub-tag.

[0052] S420 , selecting a number of given users whose number is not less than a corresponding user quantity threshold in descending order of confidence and determining them as target users to complete the expansion of the target user group.

[0053] As mentioned above, after expanding the keywords, if the number of users corresponding to the preset product sub-tags still does not meet the requirements, the keywords need to be expanded again and the logistic regression model needs to be retrained. The final number of users can be iteratively adjusted to meet business needs, so as to accurately recommend the product to more users.

[0054] At step S500, based on the user quantity threshold corresponding to each preset product sub-tag, a number of given users whose confidence levels are not less than the user quantity threshold are selected in descending order and are identified as target users to expand the target user group. Persons skilled in the art can set the user quantity threshold based on actual needs. For example, the user quantity threshold for the newborn tag is 720,000, the user quantity threshold for the 1-6 months tag is 4.32 million, and the user quantity threshold for the 1-2 years tag is 9.42 million.

[0055] To sum up, the method for expanding the target user group described in the present invention first extracts several product keywords corresponding to the first number of product description texts corresponding to the given user according to the product recommendation order of the target app of the given user, matches the several product keywords corresponding to the given user with several preset keywords in the preset keyword library respectively, and determines the target product sub-tag corresponding to the given user. By determining the target product sub-tag, the accuracy of matching the preset product sub-tag with the user can be improved. When the number of users corresponding to the target product sub-tag is less than the threshold number of users corresponding to the target product sub-tag, the preset logistic regression model is trained. By dividing the positive samples and negative samples from the product description text corresponding to the given user, the keyword expansion can be achieved. The expanded keywords are learned by the preset logistic regression model, so that the obtained target logistic regression model can screen out more users for each preset product sub-tag, thereby achieving reasonable and accurate expansion of the user group.

[0056] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0057] An embodiment of the present invention further provides an electronic device including a processor and the aforementioned non-transitory computer-readable storage medium.

[0058] Although some specific embodiments of the present invention have been described in detail by way of example, it will be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for expanding a target user group, characterized in that: The method comprises the following steps: Obtain the first number of product descriptions corresponding to each given user based on the product recommendation order of the target app for each given user, and extract a number of product keywords corresponding to each product description; For any given user, several product keywords corresponding to the given user are matched with several preset keywords in a preset keyword library, and based on the preset product subtags corresponding to the matched preset keywords, a target product subtag corresponding to the given user is determined; the preset keyword library is a library consisting of several preset keywords corresponding to the preset product subtags; When the number of users corresponding to the target product subtag is less than the threshold number of users corresponding to the target product subtag, based on the given users corresponding to each target product subtag, positive samples and negative samples are divided from the product description texts corresponding to the given users to train the preset logistic regression model to obtain the target logistic regression model; Input several product keywords corresponding to each given user into the target logistic regression model, output the number of given users corresponding to each preset product sub-label, and at the same time output the confidence level corresponding to each given user; According to the user quantity threshold corresponding to each preset product sub-tag, a number of given users whose number is not less than the user quantity threshold are selected in descending order of confidence and are all determined as target users to complete the expansion of the target user group.

2. The method for expanding the target user group according to claim 1, characterized in that: Get the preset keywords corresponding to each preset product sub-tag by following the steps below: Obtain the first several target products under the target tag category; the target tag is the parent tag of several preset product subtags; Perform word segmentation on the title text of each target product to obtain several initial keywords corresponding to each target product; Filter out a number of preset keywords from all the initial keywords based on the TF-IDF value corresponding to each initial keyword and a preset TF-IDF threshold; A preset product sub-tag corresponding to each preset keyword is received to obtain a plurality of preset keywords corresponding to each preset product sub-tag.

3. The method for expanding the target user group according to claim 1, characterized in that: The method of determining the target product subtag corresponding to a given user based on the preset product subtags corresponding to the matched preset keywords comprises the following steps: According to the preset priority corresponding to each preset product subtag, obtaining the tag priority corresponding to each preset product subtag among the preset product subtags corresponding to the matched plurality of preset keywords; The preset product sub-tag corresponding to the highest tag priority is determined as the target product sub-tag corresponding to the given user.

4. The method for expanding the target user group according to claim 1, characterized in that: The step of determining the target product subtag corresponding to the given user based on the preset product subtags corresponding to the matched plurality of preset keywords further includes the following steps: According to the preset tag priority corresponding to each preset product subtag, priority sorting is performed on the preset product subtags corresponding to the matched preset keywords to obtain a sorting result; According to a preset order-score comparison table, obtain the order score corresponding to each preset product sub-label in the sorting result; Obtaining the number of product recommendations corresponding to each preset product sub-tag in the sorting result from the first first number of recommended products corresponding to the given user; A weighted sum is calculated based on the order score, the number of recommended products, and the preset weights corresponding to the order score and the number of recommended products to obtain a final score corresponding to each preset product sub-tag in the ranking result; The preset product sub-tag corresponding to the maximum final score is determined as the target product sub-tag corresponding to the given user.

5. The method for expanding the target user group according to claim 4, characterized in that: The preset weight corresponding to the order score is greater than the preset weight corresponding to the number of product recommendations.

6. The method for expanding the target user group according to claim 1, characterized in that: The target logistic regression model is obtained through the following steps: For any target product sub-tag, randomly select a second number of given users from the given users corresponding to the target product sub-tag, and divide the product description texts of the given users into a training set and a test set according to a preset ratio; The product keywords in the product description text corresponding to the target product sub-label in the training set are determined as positive samples, and the product keywords in the remaining product description texts are determined as negative samples; Input the feature vectors corresponding to the positive samples and the feature vectors corresponding to the negative samples into the preset logistic regression model for training to obtain the initial logistic regression model; Several product keywords corresponding to each product description text in the test set are input into the initial logistic regression model, and the parameters of the initial logistic regression model are adjusted according to the output results to obtain the target logistic regression model.

7. The method for expanding the target user group according to claim 1, characterized in that: After outputting the given number of users corresponding to each preset product sub-label, the method further includes the following steps: When the number of given users corresponding to the preset product subtag is less than the threshold number of users corresponding to the preset product subtag, returning to the step of dividing positive samples and negative samples from the product description texts corresponding to all given users according to the given users corresponding to each target product subtag, so as to train the preset logistic regression model to obtain the target logistic regression model, until the output number of given users corresponding to each preset product subtag is not less than the threshold number of users corresponding to the preset product subtag; A number of given users whose confidence levels are not less than the corresponding user quantity threshold are selected in descending order and are all determined as target users to complete the expansion of the target user group.

8. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the method for expanding the target user group as described in any one of claims 1-7.

9. An electronic device, characterized in that: The device comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 8.

Images